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High Expression Levels of AGGF1 and MFAP4 Predict Primary Platinum-Based Chemoresistance and are Associated with Adverse Prognosis in Patients with Serous Ovarian Cancer

Primary platinum-based chemoresistance occurs in approximately one-third of patients with serous ovarian cancer (SOC); however, traditional clinical indicators are poor predictors of chemoresistance. So we aimed to identify novel genes as predictors of primary platinum-based chemoresistance. Gene ex...

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Autores principales: Zhao, Haiyue, Sun, Qian, Li, Lisong, Zhou, Jinhua, Zhang, Cong, Hu, Ting, Zhou, Xuemei, Zhang, Long, Wang, Baiyu, Li, Bo, Zhu, Tao, Li, Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Ivyspring International Publisher 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6360311/
https://www.ncbi.nlm.nih.gov/pubmed/30719133
http://dx.doi.org/10.7150/jca.28127
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author Zhao, Haiyue
Sun, Qian
Li, Lisong
Zhou, Jinhua
Zhang, Cong
Hu, Ting
Zhou, Xuemei
Zhang, Long
Wang, Baiyu
Li, Bo
Zhu, Tao
Li, Hong
author_facet Zhao, Haiyue
Sun, Qian
Li, Lisong
Zhou, Jinhua
Zhang, Cong
Hu, Ting
Zhou, Xuemei
Zhang, Long
Wang, Baiyu
Li, Bo
Zhu, Tao
Li, Hong
author_sort Zhao, Haiyue
collection PubMed
description Primary platinum-based chemoresistance occurs in approximately one-third of patients with serous ovarian cancer (SOC); however, traditional clinical indicators are poor predictors of chemoresistance. So we aimed to identify novel genes as predictors of primary platinum-based chemoresistance. Gene expression microarray analyses were performed to identify the genes related to primary platinum resistance in SOC on two discovery datasets (GSE51373, GSE63885) and one validation dataset (TCGA). Univariate and multivariate analyses with logistic regression were performed to evaluate the predictive values of the genes for platinum resistance. Machine learning algorithms (linear kernel support vector machine and artificial neural network) were applied to build prediction models. Univariate and multivariate analyses with Cox proportional hazards regression and log-rank tests were used to assess the effects of these gene signatures for platinum resistance on prognosis in two independent datasets (GSE9891, GSE32062). AGGF1 and MFAP4 were found highly expressed in patients with platinum-resistant SOC and independently predicted platinum resistance. Platinum resistance prediction models based on these targets had robust predictive power (highest AUC: 0.8056, 95% CI: 0.6338-0.9773; lowest AUC: 0.7245, 95% CI: 0.6052-0.8438). An AGGF1- and MFAP4-centered protein interaction network was built, and hypothetical regulatory pathways were identified. Enrichment analysis indicated that aberrations of extracellular matrix may play important roles in platinum resistance in SOC. High AGGF1 and MFAP4 expression levels were also related to shorter recurrence-free and overall survival in patients with SOC after adjustment for other clinical variables. Therefore, AGGF1 and MFAP4 are potential predictive biomarkers for response to platinum-based chemotherapy and survival outcomes in SOC.
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spelling pubmed-63603112019-02-04 High Expression Levels of AGGF1 and MFAP4 Predict Primary Platinum-Based Chemoresistance and are Associated with Adverse Prognosis in Patients with Serous Ovarian Cancer Zhao, Haiyue Sun, Qian Li, Lisong Zhou, Jinhua Zhang, Cong Hu, Ting Zhou, Xuemei Zhang, Long Wang, Baiyu Li, Bo Zhu, Tao Li, Hong J Cancer Research Paper Primary platinum-based chemoresistance occurs in approximately one-third of patients with serous ovarian cancer (SOC); however, traditional clinical indicators are poor predictors of chemoresistance. So we aimed to identify novel genes as predictors of primary platinum-based chemoresistance. Gene expression microarray analyses were performed to identify the genes related to primary platinum resistance in SOC on two discovery datasets (GSE51373, GSE63885) and one validation dataset (TCGA). Univariate and multivariate analyses with logistic regression were performed to evaluate the predictive values of the genes for platinum resistance. Machine learning algorithms (linear kernel support vector machine and artificial neural network) were applied to build prediction models. Univariate and multivariate analyses with Cox proportional hazards regression and log-rank tests were used to assess the effects of these gene signatures for platinum resistance on prognosis in two independent datasets (GSE9891, GSE32062). AGGF1 and MFAP4 were found highly expressed in patients with platinum-resistant SOC and independently predicted platinum resistance. Platinum resistance prediction models based on these targets had robust predictive power (highest AUC: 0.8056, 95% CI: 0.6338-0.9773; lowest AUC: 0.7245, 95% CI: 0.6052-0.8438). An AGGF1- and MFAP4-centered protein interaction network was built, and hypothetical regulatory pathways were identified. Enrichment analysis indicated that aberrations of extracellular matrix may play important roles in platinum resistance in SOC. High AGGF1 and MFAP4 expression levels were also related to shorter recurrence-free and overall survival in patients with SOC after adjustment for other clinical variables. Therefore, AGGF1 and MFAP4 are potential predictive biomarkers for response to platinum-based chemotherapy and survival outcomes in SOC. Ivyspring International Publisher 2019-01-01 /pmc/articles/PMC6360311/ /pubmed/30719133 http://dx.doi.org/10.7150/jca.28127 Text en © Ivyspring International Publisher This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY-NC) license (https://creativecommons.org/licenses/by-nc/4.0/). See http://ivyspring.com/terms for full terms and conditions.
spellingShingle Research Paper
Zhao, Haiyue
Sun, Qian
Li, Lisong
Zhou, Jinhua
Zhang, Cong
Hu, Ting
Zhou, Xuemei
Zhang, Long
Wang, Baiyu
Li, Bo
Zhu, Tao
Li, Hong
High Expression Levels of AGGF1 and MFAP4 Predict Primary Platinum-Based Chemoresistance and are Associated with Adverse Prognosis in Patients with Serous Ovarian Cancer
title High Expression Levels of AGGF1 and MFAP4 Predict Primary Platinum-Based Chemoresistance and are Associated with Adverse Prognosis in Patients with Serous Ovarian Cancer
title_full High Expression Levels of AGGF1 and MFAP4 Predict Primary Platinum-Based Chemoresistance and are Associated with Adverse Prognosis in Patients with Serous Ovarian Cancer
title_fullStr High Expression Levels of AGGF1 and MFAP4 Predict Primary Platinum-Based Chemoresistance and are Associated with Adverse Prognosis in Patients with Serous Ovarian Cancer
title_full_unstemmed High Expression Levels of AGGF1 and MFAP4 Predict Primary Platinum-Based Chemoresistance and are Associated with Adverse Prognosis in Patients with Serous Ovarian Cancer
title_short High Expression Levels of AGGF1 and MFAP4 Predict Primary Platinum-Based Chemoresistance and are Associated with Adverse Prognosis in Patients with Serous Ovarian Cancer
title_sort high expression levels of aggf1 and mfap4 predict primary platinum-based chemoresistance and are associated with adverse prognosis in patients with serous ovarian cancer
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6360311/
https://www.ncbi.nlm.nih.gov/pubmed/30719133
http://dx.doi.org/10.7150/jca.28127
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